Effectiveness of a multicomponent intervention to face the COVID-19 pandemic in Rio de Janeiro’s favelas: difference-in-differences analysis

Introduction Few community-based interventions addressing the transmission control and clinical management of COVID-19 cases have been reported, especially in poor urban communities from low-income and middle-income countries. Here, we analyse the impact of a multicomponent intervention that combines community engagement, mobile surveillance, massive testing and telehealth on COVID-19 cases detection and mortality rates in a large vulnerable community (Complexo da Maré) in Rio de Janeiro, Brazil. Methods We performed a difference-in-differences (DID) analysis to estimate the impact of the multicomponent intervention in Maré, before (March–August 2020) and after the intervention (September 2020 to April 2021), compared with equivalent local vulnerable communities. We applied a negative binomial regression model to estimate the intervention effect in weekly cases and mortality rates in Maré. Results Before the intervention, Maré presented lower rates of reported COVID-19 cases compared with the control group (1373 vs 1579 cases/100 000 population), comparable mortality rates (309 vs 287 deaths/100 000 population) and higher case fatality rates (13.7% vs 12.2%). After the intervention, Maré displayed a 154% (95% CI 138.6% to 170.4%) relative increase in reported case rates. Relative changes in reported death rates were −60% (95% CI −69.0% to −47.9%) in Maré and −28% (95% CI −42.0% to −9.8%) in the control group. The case fatality rate was reduced by 77% (95% CI −93.1% to −21.1%) in Maré and 52% (95% CI −81.8% to −29.4%) in the control group. The DID showed a reduction of 46% (95% CI 17% to 65%) of weekly reported deaths and an increased 23% (95% CI 5% to 44%) of reported cases in Maré after intervention onset. Conclusion An integrated intervention combining communication, surveillance and telehealth, with a strong community engagement component, could reduce COVID-19 mortality and increase case detection in a large vulnerable community in Rio de Janeiro. These findings show that investment in community-based interventions may reduce mortality and improve pandemic control in poor communities from low-income and middle-income countries.


Fundação Oswaldo Cruz (Fiocruz):
Fiocruz is the most traditional research institution on infectious diseases in Latin America, with more than 120 years of activity. Fiocruz works in research, production of inputs (vaccines, diagnostic kits, and medicines), human resources training, and innovation. Additionally, Fiocruz has several reference laboratories for infectious diseases and technological platforms that support research and innovation. More than 100 million vaccine doses were produced in 2019, and over 300,000 tests performed in reference laboratories. Fiocruz also has 1,700 doctors on staff and 323 research lines registered. Fiocruz has historically supported projects in favelas and vulnerable communities, which was intensified during the COVID-19 pandemic.

Redes da Maré:
Redes da Maré is a civil society institution that produces knowledge, projects, and actions to ensure adequate public policies to improve the lives of 140,000 residents of Maré's 16 favelas. Redes da Maré works to increase the quality of life, in an attempt to guarantee the fundamental rights of Maré population.

Saúde, Alegria e Sustentabilidade Brasil (SAS Brasil):
SAS Brasil is a nonprofit health institution created in 2013 that, in response to the COVID-19 pandemic, was offering clinical and psychological care through teleconsultations, targeting low-income populations. The SAS telemedicine project began in March 2020, involving more than 430 healthcare volunteers, distributed among 22 different medical and seven nonmedical specialties, and also performed remote consultations through their own system.

Dados do Bem (DdB):
DdB is an epidemiological monitoring project that brings together geolocation technology and methodology for real-time follow-up coronavirus evolution in urban centers. The tool generates a virus distribution map and strategic data about Covid-19 for decision-making by the authorities. Initially developed as part of a research, DdB was created by infectious disease specialists and an intelligence team, provided free of charge to the government.

Conselho Comunitário de Manguinhos: Conselho Comunitário de
Manguinhos is a neighbourhood council that aims to contribute to the sustainable development of the communities around Manguinhos. It is an autonomous body that promotes actions and debates between residents, private, governmental, and socio-community institutions.
União Rio: União Rio is a voluntary movement of civil society in Rio de Janeiro that brings together people, companies, and non-governmental organizations to preserve lives. They raise the main demands in the health area and other issues concerning vulnerable communities in order to reduce the impacts of the Covid-19 pandemic.

Supplementary Method (sMethod). Difference-in-differences analysis
Missing values: To describe patients' clinical characteristics, outcomes, and organ support, we provided their corresponding number of complete cases for incomplete variables. No imputation method was performed.
Difference-in-differences (DID) modeling: Our data comprised weekly rates of reported cases and deaths in the intervention group (Maré) and the control group (a combination of Rocinha, Cidade de Deus, and Mangueira).
To estimate the effect of the multi-component intervention, we obtained the classic difference-in-difference estimator using a Negative Binomial regression model.
Outcome: Reported number of cases and deaths per 100,000 population per age and sex Multivariable Negative binomial regression model: As our outcomes are rates, defined as the ratio between a count variable (number of events) and a denominator (population), we modeled them using a Negative binomial distribution assumption in the regression. The multivariable model syntax is defined as:

Outcomeage_strata (count) ~ Intervention/Control indicator + Period indicator + Intervention/Control indicator * Period indicator + Age group + Sex + offset(log(populationage_strata))
This model syntax means that the outcome (number of events) was explained by the indicator of the intervention (Maré) or control group, the period of intervention onset (before or after), an interaction term between the groups and the period, the age, and the sex groups. We included the log(population) as an offset variable (slope = 1) to model the outcome's denominator for the rate.
The DID estimator corresponds to the coefficient of the interaction term (intervention group * period). However, in the Negative Binomial regression, this estimate is in log scale. Hence, we obtained the DID estimator as the Rate of Rate Ratios (RRR), defined as the exp(estimate).